The 72 hour backward trajectories in Huludao City from 2019 to 2021 were simulated using the hybrid single-particle Lagrangian integrated trajectory （HYSPLIT） model.Potential Source Contribution Factor Analysis （PSCF） and Concentration Weight Trajectory Analysis （CWT） based on the daily concentration data of PM 2.5 during the same period were used to investigate the potential sources in different seasons and evaluate their contributions to the concentration of PM 2.5 in Huludao City.The results showed that the main potential sources were located in Ulan Buh Desert,followed by southeast Mongolia,eastern Inner Mongolia,Beijing-TianjinHebei region and western Liaoning in winter.In autumn,the main potential sources were southern Liaoning,Beijing-Tianjin-Hebei region,northern Shandong and northern Henan.The relatively high-value sources in spring were sporadically distributed in the Beijing-Tianjin-Hebei region,Shandong and the Bohai Sea,and they were sporadically distributed in the Beijing-Tianjin-Hebei region and northwestern Shandong in summer.

为了解高分辨率地形数据对WRF(weather research and forecasting)模式在兰州地区模拟效果的影响,本论述分别利用模型默认的GTOPO30(Digital Elevation-Global 30 Arc-Second Elevation)地形数据和分辨率为SRTM3(shuttle radar to-pography mission)地形数据对兰州市2019年1月1日～31日的气象场进行了模拟研究,并利用模拟范围内3个气象站的地面观测资料对WRF模式输出的10m风场、2m气温、地面气压和2m相对湿度进行了比较验证.结果表明:(1)10 m风场对地形数据改变比较敏感,采用SRTM3地形数据后,风速在各站点的模拟结果误差均减小,兰州站、榆中站和皋兰站的均方根误差(root mean squared error,RMSE)值相较于默认地形数据模拟结果分别降低了13.08%、21.37%、11.86%,且对风速高值模拟有一定的改善效果,郊区站点对风向的模拟效果优于城市站点;(2)两种方案对2m气温、地面气压和2m相对湿度的WRF模拟结果十分接近,使用SRTM3数据后气温误差略有降低,气压无明显变化,2 m相对湿度在城市站点模拟效果小幅改善.

在力争实现"双碳"目标的大背景下,如何制定合适的减排路径才能较好地实现甘肃省"碳达峰"目标,成为当前亟须考虑的问题.基于 2008-2017年的MEIC清单,考虑不同能源的利用效率及排放特征,对IPAT等式进行本地化修正,计算了 2005-2020年甘肃省的CO2 排放量,并设计 3种情景,研究了不同发展路径下甘肃省CO2 的排放情况.研究发现,2005-2020年甘肃省CO2 排放量呈现波动上升趋势,且煤炭对于CO2 排放的贡献最大、天然气贡献最小,石油和电力的CO2 排放贡献则在逐年升高;甘肃省CO2 排放最大的市(州)依次是兰州、嘉峪关、白银,在现有发展空间布局下嘉峪关、白银、平凉碳减排的潜力更大;在 3种发展情景中,清洁发展情景是较为适合经济欠发达的甘肃省的发展路径,此情景下全省可在2028年左右实现达峰,峰值排放量1.87亿吨,2060年CO2 排放量为峰值的50.5％.